{"id":"W3149106020","doi":"10.1002/cjce.24122","title":"Mercury removal from spent low‐level mercury catalyst by thermal treatment","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Key Laboratory for Nuclear Resources and Environment, East China Institute of Technology; Natural Science Foundation of Jiangxi Province","keywords":"Mercury (programming language); Chemistry; Environmental chemistry; MERCURE; Catalysis; Waste management; Hazardous waste; Thermal treatment; Analytical Chemistry (journal); Organic chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007474545,0.0002704753,0.0002377166,0.0002519567,0.0001554333,0.0002242416,0.0002371606,0.0002083137,0.0006744441],"category_scores_gemma":[0.0001477154,0.0001149811,0.0003022285,0.0001895821,0.0001158601,0.000130531,0.0001561653,0.0002105152,0.0002197081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002669157,"about_ca_system_score_gemma":0.0002189092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003325655,"about_ca_topic_score_gemma":0.005535703,"domain_scores_codex":[0.9998739,0.00001068821,0.00000546639,0.00001959838,0.00006101726,0.00002925146],"domain_scores_gemma":[0.9999499,0.000006832588,0.000009984122,0.000005835756,0.00001930043,0.000008056409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004974986,0.000009565164,0.0002323158,0.00003355996,0.000006306885,0.00002717564,0.000009206099,0.0001811672,0.9972874,0.00001551864,0.0000252033,0.002122837],"study_design_scores_gemma":[0.000003216894,0.00008941815,0.001265986,0.000002362036,0.00001045186,0.00003080522,0.00000782939,0.0008348188,0.9973272,0.000007398683,0.0004171197,0.000003291356],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966522,0.0005000164,0.001875824,0.00002099358,0.00001147824,0.000009268107,0.00006606663,0.00004840323,0.0008157988],"genre_scores_gemma":[0.9959618,0.0002924635,0.001431356,0.00001652216,0.000003422529,0.000006702055,0.0001450075,0.00001797703,0.002124705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003325655,"threshold_uncertainty_score":0.006612539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575577171136888,"score_gpt":0.2091348297569088,"score_spread":0.1933790580455399,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}